An Overview of the Essential Guidelines for Developing AI-Based Prediction Models in Healthcare

An Overview of the Essential Guidelines for Developing AI-Based Prediction Models in Healthcare

While far from its peak, artificial intelligence is beginning to take on more roles in healthcare. AI-driven models are helping not just plug shortages in the clinical workflow, but they are also chipping in to deliver higher diagnostic yield. With AI in healthcare having a projected 48% growth heading into 2023, it’s a no-brainer why healthcare facilities of all sizes want to make artificial intelligence part of their strategy. To want to do so is one thing, but how do you develop reliable artificial intelligence, particularly, AI-based prediction models in healthcare? Well, buckle up as we show you the ropes to get started.

First, pay great attention to your data

Algorithms run on one simple philosophy, GIGO, or garbage in, garbage out. In other words, the reliability of your prediction model is only as strong as the integrity or credibility of that data. If there’s a problem with your data sets in the first place, then chances are your AI prediction model will be problematic too. 

So before you put pen to paper, so to speak, you want to go back to the drawing board. So how much data does an AI need? The general rule of thumb in terms of how much data you need to train your model dictates the following: 

  • If your aim is to predict health conditions that may take place in 6 months, then you need to train your algorithm on a similar amount of data
  • The complexity of the task at hand also dictates how much data you need. So the more parameters you’re shooting for, then the more data samples you need
  • Our experts recommend that for every category, at least 1000 samples are in order 

With too much data comes the risk of overfitting 

Machine learning models in healthcare, just like other algorithms at large, also remain susceptible to overfitting. This happens when your ML model fits too closely to the dataset you trained it on. So why is this a problem when it sounds like a good thing at face value? Why is overfitting bad in machine learning? 

Well, that’s because data in the real world, more so in healthcare, doesn’t always exist in an ideal format (it has noise, etc.), much the same way as training data. So your AI model might ace training but falter when faced with actual test data. In other words, it may not work quite as accurately as you intend it to once the training wheels come off. 

How to solve overfitting in deep learning

Luckily, the ball’s in your court here. When we developed Rhythm AI, our ML learning model for our remote heart monitoring service, we ensured underfitting and overfitting would not be a problem by: 

  • Training with more data
  • Combining input data with noise
  • Imploring cross-validation and regularization

There are many other ways you could go depending on the tasks you want your healthcare AI model to accomplish. 

Describe the pain points you want to solve through your model 

Right off the bat, you want to define what you want to achieve with the model. You can never go wrong with SMART goals, but you also want to, more importantly, factor in the pain points you want to solve for your patients. 

If process improvement for your facility is the objective, then it’s just as important to be clear from the get-go regarding expectations and resources as well. It helps to approach your project with the mentality of a business problem.

Rely on proven software to build your ML healthcare model

Fortunately, with the many open-source software solutions available today, anyone can build a machine-learning prediction model for healthcare. That is provided they have the knowledge and skillsets. While we won’t tell you which exact one to use, some of the top machine-learning software include: 

  • TensorFlow 
  • IBM Machine Learning
  • Google Cloud AI
  • Anaconda and more 

It goes without saying that developers need to have a good grasp of programming languages such as Python and SQL/ NoSQL (for database design) to build AI models in healthcare. 

So in terms of choosing the machine learning tools for building your model, you need to choose a platform from a reputable brand with a comprehensive background. Additionally, you want to keep in the platform’s learning curve and the level of support or size of the community.  

Also, you’ll find you generally have two options to think about with platform selection. ML-as-a-service platforms and more internal development environments such as PyTorch. The former is ideal if you’re targeting rapid deployment. 

Choosing the right language for your model

The ball’s in your court as you can develop AI prediction models in a variety of programming languages, namely: 

  • Java
  • C++
  • Python, you name it

Certain dependencies should be at the back of your mind as you work out which language to move forth with. If a smooth learning curve is a huge priority for you, Python may be the way to go. On the other hand, if you want models that your team can easily debug and has great UI, Java may be a better option. 

Keep an eye on your model even after deployment

The job is only getting started once you deploy your model. You’ll want to perform continuous monitoring to ensure it’s working as intended. When we’re talking about AI prediction models in healthcare, you don’t want to deploy your model first in real-world test environments so that patients aren’t genuine pigs. 

As we mentioned earlier, once the rubber hits the road, predictive analytics may not work as intended due to environmental factors that may not have been accounted for in the training data set hence the necessity to take this extra precaution.  Consider adjusting your project parameters if your model fails to live up to expectations. 


Predictive analytics saves lives. Here at Techindia, we’re already tapping into the power of convolutional neural networks and deep learning algorithms to help in the fight against arrythmias. Our models help our ECG technicians to better validate, categorize and even predict arrythmias for our remote patient monitoring services. Hopefully, our concise guide has shed a little more light on how you can make artificial intelligence for your own healthcare needs. Contact us today for more details. 

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